Generalized Robot Learning Framework

TL;DR

This paper introduces a low-cost, reproducible robot learning framework that enables transferability across diverse robots and environments, demonstrating successful multi-task imitation learning with fewer demonstrations and proposing an objective evaluation method.

Contribution

The paper presents a novel, accessible robot learning framework that is easily transferable, supports multi-task learning with minimal data, and introduces a new evaluation metric for real-world tasks.

Findings

Successful application to industrial-grade robots

Achieved multi-task learning with fewer demonstrations

Proposed Voting Positive Rate (VPR) for objective evaluation

Abstract

Imitation based robot learning has recently gained significant attention in the robotics field due to its theoretical potential for transferability and generalizability. However, it remains notoriously costly, both in terms of hardware and data collection, and deploying it in real-world environments demands meticulous setup of robots and precise experimental conditions. In this paper, we present a low-cost robot learning framework that is both easily reproducible and transferable to various robots and environments. We demonstrate that deployable imitation learning can be successfully applied even to industrial-grade robots, not just expensive collaborative robotic arms. Furthermore, our results show that multi-task robot learning is achievable with simple network architectures and fewer demonstrations than previously thought necessary. As the current evaluating method is almost subjective when it comes to real-world manipulation tasks, we propose Voting Positive Rate (VPR) - a novel evaluation strategy that provides a more objective assessment of performance. We conduct an extensive comparison of success rates across various self-designed tasks to validate our approach. To foster collaboration and support the robot learning community, we have open-sourced all relevant datasets and model checkpoints, available at huggingface.co/ZhiChengAI.